Skip to main content

Objective Bayesian nets from consistent datasets

Landes, Jürgen, Williamson, Jon (2016) Objective Bayesian nets from consistent datasets. In: BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: 35TH INTERNATIONAL WORKSHOP ON BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING. 1757. 020007. AIP ISBN 978-0-7354-1415-0. (doi:10.1063/1.4959048) (KAR id:56779)

PDF Author's Accepted Manuscript
Language: English
Download (138kB)
[thumbnail of LandWDI.pdf]
This file may not be suitable for users of assistive technology.
Request an accessible format
Official URL:


This paper addresses the problem of finding a Bayesian net representation of the probability function that agrees with the distributions of multiple consistent datasets and otherwise has maximum entropy. We give a general algorithm which is significantly more efficient than the standard brute-force approach. Furthermore, we show that in a wide range of cases such a Bayesian net can be obtained without solving any optimisation problem.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1063/1.4959048
Subjects: B Philosophy. Psychology. Religion > BC Logic
Q Science > QA Mathematics (inc Computing science) > QA273 Probabilities
Divisions: Divisions > Division of Arts and Humanities > School of Culture and Languages
Depositing User: Jon Williamson
Date Deposited: 10 Aug 2016 08:42 UTC
Last Modified: 09 Dec 2022 08:55 UTC
Resource URI: (The current URI for this page, for reference purposes)
Williamson, Jon:
  • Depositors only (login required):


Downloads per month over past year